Project description

SCALABLE ONLINE MACHINE LEARNING

PROTEUS will contribute to the maturity of this field by designing and developing a library of Scalable Online
Machine Learning and Data Mining Algorithms (named SOLMA) adapted to the data analytics platform, Apache Flink. The SOLMA library will consist of efficient distributed online algorithms for basic
utilities, sketches as well as advanced online predictive analytics for various tasks like classification, clustering, regression, ensemble methods, and novelty and change detection.

In particular PROTEUS will not address only scalability, but also complexity. While for scalability various
computational concepts will applied to develop highly scalable streaming algorithms, for complexity we will consider distributed multivariate streams where data is potentially complex (text,
picture, video) to reflect on the third element of big data which is variety. All algorithms developed will be theoretically analysed to determine their bounds. SOLMA will be enriched with a set of techniques that will enhance the applicability of the algorithms in various real-world situations by considering drift
handling, novelty detection, active learning and semi-supervised learning.

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NEWS & EVENTS

PROTEUS, jointly with other initiatives and project (like our sister projectTOREADOR) will be jointly
organize a workshop on Industrial Data Platforms for the Manufacturing domain during theEuropean Big Data Value Forum(EBDVF), held in
Versailles (Palais des Congrès), France from 21st to 23rd November 2017. The EBDVF results of a joint initiative between theBig Data Value Association (BDVA)and theEuropean Data Forum (EDF), as a key European event for industry professionals, business developers, researchers, and policy makers to discuss the challenges and opportunities of
the European data economy and data-driven innovation in Europe.